Financial Structure, Corporate Finance, and Growth …conference/conference2004/...Further, Levine...
Transcript of Financial Structure, Corporate Finance, and Growth …conference/conference2004/...Further, Levine...
Financial Structure, Corporate Finance, and
Growth of Taiwan’s Manufacturing Firms
Wan-Chun Liu
Takming College
e-mail: [email protected]
Chen-Min Hsu
National Taiwan University
e-mail: [email protected]
Financial Structure, Corporate Finance, and
Growth of Taiwan’s Manufacturing Firms
Wan-Chun Liu and Chen-Min Hsu *
Abstract
The purpose of this paper is to examine the determinants of Taiwan’s manufacturing firm growth, in particular, the effects of financial structure, corporate financing choices and Taiwanese outward FDI in China on firm growth in different industries besides other physical factors discussed in the literature. We construct an unbalanced dynamic panel data using 280 listed and OTC manufacturing firms over the period 1991-2002. The empirical method utilized is the generalized method of moments (GMM) proposed by Arellano and Bond (1991). Our results find that (1) the growth rates of firms are positively related to firm size, age, capital intensity, lagged R&D, export ratio, investment ratio, and profits; (2) high debt to equity ratio is associated with low corporation growth, while high return on total assets is associated with high corporation growth, which reflects that a firm with a relatively sound financial structure will facilitate their growth; (3) higher liquidity of stock market relative to the banking sector lead to higher growth of firms. However, larger size of stock market relative to the banking sector leads to lower the firm’s growth, i.e., the smaller the indirect finance, the lower the firm growth; (4) traditional industrial firms engaged in FDI toward China had complementary effects on their output growth, in contrast, basic industrial and technology-intensive firms engaged in FDI toward China might be substituted; (5) individual firms that could be financed more from either bank or equity market will enjoy higher rates of growth compared to others in the same industries, but, those effects on traditional and basic industries are weaker; (6) high bank-financing ratio and internal financing are associated with higher firm growth, while firms using more bonds or equity financing tend to experience lower growth. However, the net positive effects of equity financing on traditional and basic firm growth are significantly greater. JEL classification: C33; D92; F23 Keywords: Financial structure; Corporate finance; Firm growth; Outward FDI; GMM estimation.
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* Wan-Chun Liu is assistant professor of international trade at Takming College, Taipei. Chen-Min Hsu is professor of economics at National Taiwan University. E-mail address: [email protected].
1. Introduction
Taiwan’s financial system was deregulated slowly and gradually until the
mid-1980s. As a consequence of financial liberalization, internationalization and
diversification, there has been a significant structural change in the Taiwan’s financial
system. According to financial statistics data, the share of intermediary financing
dropped from 90.87% in 1990 to 71.25% in 2003. In contrast, the share of direct
financing from the financial market rose from 9.13% to 28.75% during the same
period. This indicates that the dependence on intermediary financing for Taiwanese
firms has been declining. And they have significantly shifted from intermediary
financing to direct financing, i.e., from bank loans to financial market issues.
In addition, due to the deregulation of equity market in 1988 and gradual capital
account openness since 1987, the number of listed firms increased quickly in the past
decade. For example, in comparison with the situation of 203 listed and over-the-
counter (OTC) companies prior to 1990, there are 1092 listed and OTC companies in
2003. Also, the ratio of total par value of listed shares to total loans of financial
institutions changed drastically from 10.93% in 1990 to 37.75% in 2003. The share of
corporate bond issues have increased and been substituted for the loans from financial
institutions since 1996. It appears that corporate finance channels are different. These
data show that financial structure has been alternated. It is thus interesting to see
whether the effects of financial structure change and corporate financing patterns on
the growth of Taiwanese firms are significant. This is due to the fact that through the
financial channels, financial structure and corporate finance would influence
corporate investment, resource allocation and the growth of firms, and hence the
economic growth.
Most of the literature on the determinants of firm growth have mainly focused on
the relationship between initial firm-specific conditions and firm growth. For example,
Singh and Whittington (1975) examined the relationship between the size of firms and
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their growth for nearly 2000 British firms in 1948-1960 and found that firm size had a
significant positive effect on firm growth. Later, based on the profit maximization
problem, Jovanovic (1982) established a theoretical model of firm learning to analyze
the survival of firms. He showed that firm age and size were important factors in
determining the survival of firms. Also, smaller firms grew faster but were more likely
to fail than large firms.
Evans (1987a,1987b), Hall (1987) and Dunne et al. (1989) applied the theoretical
model of Jovanovic (1982) to test the relationships among the U.S. manufacturing
firm growth, firm size, and firm age. They found that firm growth decreased with firm
age and firm size. The inverse relationship between growth and age is consistent with
Jovanovic (1982). Variyam and Kraybill (1992) and Dunne and Hughes (1994) also
obtained similar results using the U.S. manufacturing, sales and service firms data and
the U.K. manufacturing data, respectively.
Some studies on firm growth started to focus on the elements of innovation and
R&D in the mid-1990s. For example, Audretsch (1995) showed that the post-entry
performance of new firms and technological conditions were closely related.
Specifically, they found that higher innovative environment was associated with
higher survival and growth. Audretsch and Mahmood (1995) also found that R&D
intensity was positively correlated with firm’s survival. Lee and Shim (1995) showed
that the relationship between firm growth and R&D expenditure was significantly
positive using the high-tech firm data from U.S. and Japan. Griliches and Mairesse
(1983), Hall and Mairesse (1995), Raut (1995) and Yang and Chen (2002) also
examined the relationship between R&D and productivity growth of firms.
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The relationship between outward foreign direct investment (FDI) and firm
growth was also under investigation. For example, Chen and Ku (2000) used the
firm-level data and estimated the effect of FDI on the growth performance of FDI
firms from Taiwan. They found that FDI would strengthen rather than weakening the
viability and competitiveness of domestic industries. Furthermore, by employing 271
data from British firms during 1976-82, Paul et al. (1997) found that the current
period growth rates and a natural measure of changes in current expectations about
long run profitability (namely, changes in the stock market valuation of the firm) are
robustly positively correlated.
Corporate finance theory suggests that market imperfections, as well as
information and incentive problems raise the cost of external finance, especially due
to underdeveloped financial and legal systems. These may constrain firm’s ability to
fund investment projects. (See Myers and Majluf (1984).) By utilizing firm-level data,
Demirgüc-Kunt and Maksimovic (1998) showed the importance of the financial
system and the rule of law for relaxing firms’ external financial constraints and
facilitating growth. Rajan and Zingales (1998) used industry-level data to show that
industries that are dependent on external finance grow faster in countries with a
developed financial system. Beck et al. (2002) employed firm-level survey data for 54
countries, to investigate whether financial, legal and corruption obstacles affect firm
growth. They showed that underdeveloped financial and legal systems and higher
corruption could obstruct firm growth. Greenwood and Jovanovic (1990), Beck et al.
(2000), Levine et al.(2000) and Christopoulos and Tsionas (2004) showed that
financial system would enhance the investment efficiency and productivity.
Particularly, financial development will enhance fund liquidity and risk dispersion.
Further, Levine (2000) and Beck et al. (2001) examined the relationship between
financial structure and economic development and found that the effect of bank
finance and equity finance on corporate growth was ambiguous. Beck et al. (2001)
also showed that firm growth was primarily affected by internally generated funds and
short-term debts, while it was less affected by the cost of external finance. It reveals
that sources of finance and firms growth are closely related. It is true that financial
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development create more financing channels and reduce the gap between external and
internal costs of funds to firms and make firm investment more efficient, promoting
firm growth. Thus, corporate financing choices have significant effects on firm
growth.
The purpose of this paper is to investigate the determinants of manufacturing
firm growth. Although the existing literature has already provided many physical
elements on the effects of firm growth, some important financial factors are still
unexplored, for example, financial structure changes and corporate financial patterns.
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In this paper, we take Taiwan as an example to explore the role of financial
factors in the firm growth process. It is noteworthy that Taiwan has experienced a
dramatic economic structural change since 1986. The share of industrial sector in
GDP gradually fell, while the share of service sector increased rapidly. In the
meantime, the percentage of GDP originating from manufacturing fell from the peak
39.35% in 1986 to 25.85% in 2002. Furthermore, as Taiwanese firms making
substantial outward foreign direct investment in Southeast Asia and China since the
late 1980s, the manufacturing structure had remarkably changed. The traditional
industry as a fraction of manufacturing GDP shows a decreasing trend, while
technology-intensive industry exhibits a growth trend. According to the Taiwan
national income statistics data, the traditional industries (including food and beverage
processing, tobacco, textile, garment and footwear, leather and fur products, lumber
and bamboo products, and paper products and printing) have dropped from 28.72% of
manufacturing GDP in 1990 to 14.90% in 2002. In contrast, the share of
technology-intensive industries (including machinery equipment, electrical, electronic
machinery and repairing, transport equipment, and precision instruments) in
manufacturing GDP rose from 30.47% to 42.65% during the same period. Likewise,
the share of technology-intensive industry in manufacturing GDP has surpassed that
of traditional industry since 1990, and the technology-intensive industry has been
continuously growing in 1990s. Clearly, technology-intensive industry has played a
critical role in the process of Taiwan’s economic development.
Due to remarkable shifts in manufacturing industries and the continuous growth
in Taiwanese foreign direct investment (FDI) toward China, our study also aims
further to analyze whether the effects of corporate financing choices and Taiwanese
outward FDI in China on firm growth in different industries are crucial.1 We
construct an unbalanced dynamic panel data using 280 listed and OTC manufacturing
firms in the Taiwan Stock Exchange Corporation (TSEC) over the period 1991-2002.
The empirical method utilized is the generalized method of moments (GMM)
proposed by Arellano and Bond(1991).
The rest of the paper is organized as follows. Section 2 describes the empirical
model and data. Section 3 presents and summarizes the estimation results. Section 4
concludes the paper.
2. Empirical Model and Data Description
2.1 Empirical Model and Method
In this section, we examine the determinants of firm growth by extending the
work in Gallego and Loayza (2001) to include indices of financial structure, outward
FDI and corporate financing sources. The empirical model could be expressed as
follows:
Yit =α0+α1Yit-1+β’ Xit+γ FDIit+π’Fit+λ’ Zt+ IND∑=
2
1jjϑ j×FDIit
+∑ IND=
2
1jjϑ j×Fit+µi+εit, i=1,…,n; t=1,…,T, (1)
where the subscripts i and t refer to firm and time respectively. Dependent variable Y
is the firm growth. Explanatory variables X stands for the variables capturing
firm-specific characteristics, including firm age, firm size, firm’s capital-intensity,
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1 The FDI flows from Taiwan to China increased dramatically from $174 million in 1991 to $4.59 trillion in 2003. China has become the major target of outward investment of Taiwan.
R&D ratio, export ratio, investment ratio and profitability. FDI captures the effect of
firm’s FDI toward China. F stands for the factors capturing individual firm’s
financing sources and the credit degree of individual firm in bank sector and stock
market. Z captures the macroeconomic variables, which only vary with time but not
across firms, including business fluctuations, financial development and structure
variables. IND is the industry dummy variables to catch the growth differences across
industries. We also incorporate the interaction of financing source with the industry
dummy in our model to test whether the effects of corporate financial choices on firm
growth in traditional, basic, and technology-intensive industries are different. µI and
εit are the firm-specific fixed effect and error terms.
To eliminate the firm-specific effect that might cause the biases of estimators, we
estimate first-differences of equation (1). Since the variables may be endogenous,
using OLS estimates will lead to inconsistency. We employ a dynamic panel,
generalized method of moment (GMM) estimator proposed by Arellano and Bond
(1991). They have shown that the consistency of the GMM estimator depends on the
validity of the instruments and the assumption that the differenced error terms do not
exhibit second order serial correlation. To test these assumptions, they proposed a
Sargan test of overidentifying restrictions, which tested the overall validity of the
instruments by analyzing the sample analog of the moment conditions used in the
estimation procedure. Besides, they also tested the assumption of no second-order
serial correlation. Failure to reject the null hypotheses of both tests gives support to
our estimation procedure. All regressors are treated as endogenous except the macro
variables, dummy variable, the interaction term of dummy variable, firm age and firm
size which are strictly exogenous. Therefore, we conduct the analyses with lagged
independent variables dated t-2 and earlier together with the lagged changes of
endogenous variables, and exogenous variables used as instruments variables.
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2.2 Data
Since we hope that the sample period is not too short and the sample size is not
too small, we construct an unbalanced dynamic panel data using 280 listed and OTC
manufacturing firms in the TSEC over the period 1991-2002.
From the total sample, we deleted all the observations that did not have a
complete record in the variables included in our analysis. Likewise, we deleted a
small number of observations with negative values for the replacement cost.
Furthermore, we also eliminated firms that were in sample for less than five years.
After adjustments, the companies included in our sample are 280 with 2519
observations. 2 Financial information from annual reports of the firms is drawn from
the “ TEJ (Taiwan Economic Journal) Financial Statement of Listed Companies in
TSE Data Bank” and “TEJ Production and Sales Data Bank”. Data on the market
value of the firm’s stock is obtained from “TEJ Stock Price Data Bank”.
The definitions of dependent and explanatory variables and the associated
measurement are discussed below. The dependent variable in the analysis of firm
growth is the firm’s net sale growth rate. The growth of sale is used as a proxy for that
of output. It captures the performance of the firm growth.
The independent variables can be grouped in four different categories: (1)
firm-specific characteristics; (2) foreign direct investment; (3) macroeconomic factors
and industry effect; (4) firms’ financing sources, and the credit degree of individual
firm in the banking sector and stock market.
Among the firm-specific characteristics, the first variable is the firm age (Age).
Firm age is defined as the time period since the date of incorporation. The second
variable is the natural logarithm of the quantity of firms’ employees as an indicator of
firm’s size (Size). Empirical studies on domestic firms, however, find a mixed
82 There are 509 listed and OTC manufacturing firms in the TSEC at the end of 2002.
relationship between firm size and growth. Some supports a positive relationship (e.g.
Singh and Whittington (1975)), but negative relationship seems to dominate the
empirical literature (e.g. Evans (1987a,1987b), Hall (1987), Dunne et al. (1989), and
Variyam and Kraybill (1992)). The third variable, reflecting firm characteristics, is the
capital intensity (KL) and is measured by the ratio of capital stock to the number of
employees. We use the replacement cost of fixed assets as a proxy of capital stock. To
obtain replacement cost, we adopt the method proposed by Chan (2002) to adjust the
face values of fixed assets. 3
The fourth variable is R&D ratio (R&D), which is the ratio of R&D expenditure to
sale. Theoretically, R&D helps firms upgrade technology and enhances their
capability in product innovation. The sign of R&D ratio is expected to be positive.
The fifth variable is export ratio (Export) and is measured by the exports to the sale
ratio. High export ratio reflects better international competition and productivity. We
therefore expect the export ratio to be positively related to firm growth. The sixth
variable is the firm’s investment ratio (Investr) and is defined as the ratio of firm’s
investment to revenues. The seventh variable is capturing the firm’s leverage and is
defined as the debt to equity ratio (Debt). The index of debt to equity ratio was
constructed by Schmukler and Vesperoni (2001) to examine the determinant of firm
growth. Finally, the return on total assets (Profit) is considered to capture the capacity
of firm to generate internal resources and is defined as firm’s profits after taxes
divided by average total assets.
The variable in the second category measures the effect of firm’s FDI toward
China (FDI). Since data on the amount of firm’s outward FDI toward China are not
available, we use a dummy variable, which is time-invariant and that takes the value
one from the moment that a firm has engaging FDI in China, and zero otherwise.
93 See Chan (2002), p57.
The third category involves macroeconomic factor and industry dummy that affect
firm growth. The first variable captures the business fluctuation conditions and is
measured by the growth rate of real GDP (Gdpgr). The economic boom period, (that
is, period with high GDP growth) is associated with a higher level of sale. We expect
that the growth rate of real GDP is positively related to the firm growth. The second
variable related to macroeconomic factors is the one capturing financial intermediary
development. Two indicators of financial intermediary development are constructed.
First, we use a broad money stock (M2) to GDP ratio (M2gdp) to capture the overall
size of the formal financial intermediary sector. This is a typical indicator of financial
depth (see King and Levine (1993)). Second, we use bank claims on the private sector
divided by GDP (Private), which is an indicator of bank activity in the private sector.
The measure excludes loans issued to governments and public enterprises. It also
excludes credits issued by the central bank. It indicates the share of credit funneled
through the private sector.4
The third macroeconomic variable is related to the stock market development.
Two indicators are considered. The first is the stock market capitalization ratio
(Marcap), which is the ratio of the market value of listed shares to GDP. This is a
typical measure of stock market size. The second indicator is the total value traded as
a share of GDP (Valtrade). The index measures the value of stock transactions relative
to the size of the economy. It is frequently used as a measure of stock market
liquidity.5
The last macroeconomic variable captures the financial structure. We use two
measures of financial structure, i.e., structure-size (Structs) and structure-activity
(Structa), which were constructed by Demirgüc-Kunt and Levine (2001). Structs
indicates the size of stock markets relative to the size of the banking sector and is
4 See Levine et al. (2000), Beck et al. (2000), and Hsu et al. (2003).
105 See Levine and Zervos (1998) and Hsu et al. (2003).
defined as the natural logarithm of Marcap divided by Private. Structa measures the
relative importance of bank and stock market finance and is defined as the natural
logarithm of Valtrade divided by Private. A larger stock market relative to banking
sector suggests that relatively well-developed stock markets could substitute for bank
finance.6
In addition, we also include industry dummies variables to reflect the firm growth
difference across industries. According to firms’ attribution, we divide the firms into
three industries—traditional (including cement, foods, glass and ceramics, textiles,
paper and pulp), basic (including chemicals, rubber, plastics, steel and iron), and
technology-intensive (including electric and machinery, electric appliance and cable,
automobile, and electronics) industries. 7 Two dummy variables— IND1 and IND2
are created to differentiate these industries. IND1 is given a value of one for
traditional industry, whereas the others possess zero. IND2 takes one for basic
industry, otherwise zero.
The fourth category involves variables that reflect firms’ financing sources and the
credit degree of individual firm in the banking sector and stock market. Four variables
measure firms’ financing sources. The first variable is bank-financing ratio (Bank),
which is used as a proxy for external finance. It is defined as the percentage of firm’s
financing from banks.8 The second and third variables are both equity-financing ratio
(Stock) and corporate bond-financing ratio (Bond). They also capture external finance
and are defined as the percentage of firm’s financing from stocks and corporate bonds
issues, respectively. The last variable is internal funds ratio (Retain) and is defined as
the percentage of firm’s financing from retained earnings. Due to the fact that the sum
6 Real GDP is from National Income in Taiwan Area, ROC. Bank claims on the private sector is from Financial Statistics, Taiwan District Republic of China (compiled in accordance with IFS format), CBC. The market value of listed shares and total value traded are from http://www.sfc.gov.tw/7-1.htm. 7 We follow Chan (2002) to classify the industries.
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8Total funds are composed of bank borrowing, corporate bonds issuing, stock issuing and retained earnings.
of Bank, Stock, Bond and Retain are one, when adding them together in the regression,
multicollinearity might be serious. To solve the problem, we use internal financing
(Internal) as a proxy for internal funds ratio. 9 Internal is defined as retained earnings
over total liabilities and expresses the importance of internal financing. The index of
internal financing was constructed by Schmukler and Vesperoni (2001).
Finally, we use two indicators to measure the credit degree of individual firm in
the banking sector and stock market. The first variable is firm’s share of bank
borrowing (Sdebt) and is defined as the ratio of bank borrowings of firm relative to
total bank borrowings of the industry. 10 High values of the index reflect that it is
easier for a firm to borrow from banks with higher credit degree. The second variable,
firm’s share of stock market value (Smar), is defined as the ratio of firm’s stock
market value to the total stock market value of the industry. It would provide the share
of the firm in a stock market, as a measure of information of stock market size for
individual firm relative to other firms in the same industry. The index reflects the
degree of stock credit for an individual firm.
2.3 Basic Statistics
Table 1 presents the number of listed and OTC companies by industry in various
years. There are only 106 listed companies in 1991, among them, the largest share is
the traditional industry followed by the basic industry. The number of listed and OTC
companies in the traditional industry was still the largest share and accounted for
39.47% of our sample in 1994. The number of firms in technology-intensive industry
continued to grow. By 1995, it was the largest industry in our sample. The number of
technology-intensive firms was only 62 in 1995, but had reached 146 and accounted
9 The correlation between internal financing and internal funds ratio is high. Hence, we use internal financing index is being substituted for internal funds ratio.
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10Bank borrowing is calculated as the sum of long-term debt and short-term debt minus nonfinancial institutions debt.
for 52.14% of our sample in 1998.
Table 2 reports the sources of financing by industry in various years. Four forms
of financing sources are bank borrowing, stock issuing, corporate bonds issuing and
internal funds.11 When comparing the difference of financing source, we find that
financing through the share of stock issuing is the largest one, accounting for over
60% of total funds. It reflects that firms prefer to rely more on stock as a source of
fund. After the shocks of Asian financial crisis and local financial crisis in 1998, the
profitability of the listed and OTC companies in Taiwan weakened and therefore the
internal funds got reduced further. Especially, the traditional industries, the mean
retained earnings over total funds became negative since 1999. As for the bank
borrowings, the traditional and basic industries have the highest average bank
borrowing share during the study period except in 1995-1996. Regarding the stock
issuing, the traditional and basic industries also have a high average stock issuing
share except in the year 1995. In addition, the share of corporate bonds issuing in the
technology-intensive industry shows a significantly increasing trend from 5.03% in
1996 to 8.55% in 2002, while the traditional industry shows a declining trend. As for
the internal funds, the technology-intensive industry has the highest average internal
funds ratio (3.27%).
Table 3 shows the summary statistics for major variables. Over the sample period,
firms of technology-intensive industry have much higher average growth rates of
output, firm size, R&D ratio, export ratio, investment ratio, and rate of return on total
assets than the other industries. However, firms in technology-intensive industry have
lower average age and debt to equity ratio. In addition, bank borrowing and equity
issues are the most important financing sources for firms. Technology-intensive
industry appears to have more bonds and internal financing as sources of funds. One
1311 Internal funds are the retained earnings.
possible reason may be that technology-intensive firms are newly established with
more cash inflows and high market credit.
3. Empirical Results
Table 4 reports the correlations matrix. The cross-correlations are markedly
higher in several variables. First, Gdpgr is negatively and highly correlated with
M2gdp, with a correlation coefficient of -0.75. Second, the correlation between Debt
and Bank is 0.76. Third, Profit is correlated with Internal, with a correlation
coefficient of 0.83. Fourth, Marcap is positively and strongly correlated with Structs,
with a correlation coefficient of 0.97. The correlation between Valtrade and Structa is
also extremely high (0.97). Finally, the correlation between Structa and Structs is 0.63.
It appears that these variables are highly correlated over the sample period. Hence,
when we add them in each of the regression, multicollinearity might be serious. To
overcome the difficulty, only one of them was included in each of the regressions.
Table 5 reports the regression estimation results for the firms’ growth. The
asterisks ** indicate the error level at 0.05, all in a one-tail test. Almost all one-step
GMM estimation results reject the over-identifying restrictions. Table 5 hence shows
the two-step GMM estimation results. The last two row shows p-values of the
two-step difference for the Sargan test of the overidentifying restrictions. All p-values
of the Sargan test cannot reject the null of overidentitying restrictions. Thus, we don’t
reject the null hypothesis that the instruments are appropriate. The last row reports p-
values of the test of second-order serial correlation. The tests indicate that
econometric specification and the assumption of no second-order serial correlation
cannot be rejected.
As shown in Table 5, the results show a significantly positive relationship
between firm age, size, capital intensity, lagged R&D, investment ratio and firm
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growth.12 This suggests that larger and older firms have better growth performance
than smaller and younger firms. Higher lagged R&D, investment ratio and capital
intensity are associated with higher firm growth. The Export has a positive and
significant effect on firm growth. As Taiwan is a small open economy with a high
degree of trade dependency, manufacturing sector is the major exporting sector.
Higher export ratio has contributed to higher growth for these firms. Further, the
return on total assets has significantly and positively affected on firm growth, while
the debt to equity ratio has a negative effect on the growth of firms. The results
indicate that if a firm has a relatively sound financial structure, they will enjoy higher
growth.
As to the effect of the financial development, Columns (3)~(4), include the
effects of both bank development and stock market development indicators. We find
that the coefficient for Private is significantly positively related, while M2gdp,
Marcap and Valtrade are significantly negatively related to the firm growth. It is
shown that the development of the banking sector is more relevant than that of the
stock market for the growth of the firm. These indicate that Taiwan stock market
during 1991-2002 is still a underdeveloping and emerging stock market.13 The results
are consistent with previous research documented by Gallego and Loayza (2001),
when they studied the case of Chile. Similar results are obtained when we replace the
financial development by financial structure in Columns (5)~(6). In Column (5), the
estimated coefficient of structure-size (Structs) has a negative and significant effect on
the growth rate of firms, while in Column (6) the estimated coefficient of
12 Current R&D has been considered as one of the explanatory variables. But all coefficients are significantly negative. However, all coefficients of lagged R&D dated t-1 are significantly positive and are quite robust. The positive impact of R&D on firm growth might be effective one year later. We hence use lagged R&D date t-1 in our analysis.
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13 In fact, the liberalization of stock market in Taiwan was slowly and gradually during the past decade. The participation of foreign investors in the Taiwan stock market is also allowed to increase gradually. Until 2003, the government has pushed for greater liberalization of the stock market. QFIIs were permitted to invest up to 100% in most listed firms in Taiwan, while the qualification requirement for each QFII was abolished.
structure-activity (Structa) is significantly positive. It reflects that larger activity of the
banking sector and stock market lead high growth of firms.
To capture the effect of outward FDI toward China on firm growth, in Columns
(7)~(8), we add the FDI dummy variable. The results indicate that firm investment in
China has a negative effect on firm growth. This reflects that outward FDI toward
China has a substitution effect on firm’s domestic output. In addition, to further
examine whether the relation between outward FDI toward China and firm growth
differs across industries, we consider the interaction term of FDI and industry
dummies in our regression. The results indicate that the growth of firm is positively
related to both the interaction terms FDI × IND1 and FDI × IND2. Moreover, the
net effect of the FDI of traditional industry on firm growth is significantly positive,
but significantly negative in basic industry. This result indicates that the traditional
industrial firm engaged in FDI toward China has experienced complementary effects
on their output growth. In contrast, the effect of basic industrial firm engaged FDI
toward China might be substituted. It is also substitute for technology-intensive
industry. These results seem to that FDI toward China might be hollowing-out in the
basic and technology-intensive industries, while it is complementary for traditional
industry.
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In addition, using firm’s share of bank borrowing (Sdebt) and that of stock
market value (Smar) as proxies for the credit degree of firm in the banking sector and
stock market respectively, Column (9) indicates that a firm’s share of bank borrowing
(Sdebt) and that of stock market value (Smar) have significant positive effects on the
growth of that firm. It indicates that individual firm that could be financed more from
either banks or equity market will enjoy high rate of growth compared to other firms
in the same industry. Also, we examine whether those effects are different across
industries. Hence, we include the interaction terms with industry dummies in the
regression, as shown in Columns (10) and (11). The estimated coefficients for the
interaction term IND1 × Smar and IND2 × Sdebt become significantly negative, and
the interaction terms IND1 × Sdebt are insignificantly negative, while the IND2 ×
Smar interaction terms are significantly negative. The net effects of Sdebt and Smar
for firms in the traditional or basic industries are significantly positive. However, the
estimated coefficients for Smar are lower than those for firms in the
technology-intensive industry. Thus, this indicates that the effect of equity financing
on firm growth are weaker for traditional and basic industries than for
technology-intensive industry.
Regarding financing sources, in Columns (12)~(16), all coefficients of internal
financing (Internal) and bank-financing (Bank) are significantly positive. But when
considering firm’s financing sources from bond-financing (Bond) or equity-financing
(Stock), firm growth is significantly negatively related to both Bond and Stock. This
implies that firms with more retained earnings and lower equity financing are likely to
have higher growth. In addition, firms capable of using bank financing also have
higher growth. Similar results are found in many industrialized countries. 14
Moreover, since the effects of financing source on firm growth may differ from
industry to industry, the interaction terms of industry dummy with financing patterns
are included in regressions. The results show that coefficients of these estimators for
the interaction terms Bank × IND1, Bank × IND2, Bond × IND1, Bond × IND2,
Internal × IND1, and Internal × IND2 are negative and statistically significant,
while the coefficients for the interaction term Stock × IND1 and Stock × IND2 are
significantly positive. The net effects of various financing sources appear to be
positive and significant for firms in traditional and basic industries except bond
financing.
17
14Data in U.S., U.K., and Germany indicate that in the long run the bank-financing share is higher for corporate finance than other external financing. (See Mishkin (2004)).
4. Conclusions
Using a firm-level panel data, the paper examines the determinants of firm
growth for 280 listed and OTC manufacturing companies in Taiwan over the period of
1991-2002. Apart from firm characteristics, we also focus on the effects of financial
structure, corporate financing pattern and outward FDI in China on the growth of
Taiwanese manufacturing firms. The present findings can be summarized as in the
following.
First, it shows that firm characteristics including age, size, capital-intensity,
lagged R&D, investment ratio, return on total assets and export ratio have
significantly positive effects on firm growth. Second, the study shows that high debt
to equity ratio is associated with lower corporation growth, while high profitability of
total assets is associated with higher firm growth. This reflects that a firm with
financial structure relative sound will facilitate its growth.
Third, higher liquidity of the stock market relative to the banking sector leads to
higher growth of firms. However, larger size of the stock market relative to the
banking sector leads to lower firm’s growth during 1991-2002. This implies that the
substitution of direct finance for indirect finance is harmful to firm growth. Fourth,
traditional industrial firms engaged in FDI toward China had experienced
complementary effects on their output growth. In contrast, firms in basic and
technology-intensive industries engaged in FDI toward China might be substituted.
Fifth, those firms that could be financed more from either banks or equity market will
enjoy higher rates of growth compared to other firms in the same industry. However,
the effects on traditional and basic industries are weaker than those on
technology-intensive industry.
Finally, high bank-financing ratio and internal financing are associated with
higher firm growth, while firms using more bond or equity financing tend to
experience lower growth. However, it should be noted that the net effects of equity
18
financing on the growth of firms in traditional and basic industries are significantly
positive and greater during the study period. The relatively low productivity and low
ratio of bank financing in those two industries during 1990s explain such results. The
fact that traditional and basic industries have confronted the contraction of banking
loans is thus quite reasonable.
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22
Table 1 Number of Firms by Industry and Year Unit: No. of firms
Industry Year
Traditional Industry
Basic Industry
Technology- intensive industry
Total
47 31 28 106 1991
(44.34%) (29.25%) (26.42%) (100%) 51 40 33 124
1992 (41.13%) (32.26%) (26.61%) (100%)
55 43 37 135 1993
(40.74%) (31.85%) (27.41%) (100%) 60 45 47 152
1994 (39.47%) (29.61%) (30.92%) (100%)
62 49 62 173 1995
(35.84%) (28.32%) (35.84%) (100%) 65 53 81 199
1996 (32.66%) (26.63%) (40.7%) (100%)
68 57 105 230 1997
(29.57%) (24.78%) (45.65%) (100%) 72 62 146 280
1998 (25.71%) (22.14%) (52.14%) (100%)
72 62 146 280 1999
(25.71%) (22.14%) (52.14%) (100%) 72 62 146 280
2000 (25.71%) (22.14%) (52.14%) (100%)
72 62 146 280 2001
(25.71%) (22.14%) (52.14%) (100%) 72 62 146 280
2002 (25.71%) (22.14%) (52.14%) (100%)
Note: Figures in parentheses are percentages. Source: Computed by author from data in TEJ.
23
Table 2 Distribution of Sources of Financing by Industry Bank-
Financing %Equity-
Financing %Bonds-
Financing % Internal Funds%
Traditional 25.71 64.85 1.63 7.80 Basic 22.23 67.30 0.94 9.54 1991
Technology 21.90 60.02 4.08 13.99 Traditional 24.59 67.00 1.66 6.75
Basic 22.77 67.01 1.34 8.88 1992 Technology 20.79 63.59 3.40 12.21 Traditional 22.04 70.41 1.55 6.01
Basic 20.54 68.59 1.07 9.80 1993 Technology 18.57 63.15 2.91 15.37 Traditional 20.69 67.78 1.69 9.84
Basic 20.01 65.09 3.04 11.87 1994 Technology 19.09 60.09 4.67 16.16 Traditional 19.93 71.35 1.44 7.28
Basic 20.19 64.98 3.79 11.05 1995 Technology 23.36 71.75 7.03 -2.13 Traditional 17.37 72.81 2.98 6.84
Basic 18.53 64.74 6.12 10.60 1996 Technology 18.56 60.95 5.03 15.45 Traditional 19.71 67.96 3.57 8.77
Basic 23.34 61.08 7.22 8.36 1997 Technology 19.15 57.97 5.80 17.08 Traditional 23.40 71.59 3.47 1.54
Basic 22.90 65.13 6.69 5.29 1998 Technology 20.44 64.36 5.83 9.37 Traditional 25.73 70.78 3.88 -0.38
Basic 24.54 64.48 6.18 4.80 1999 Technology 20.46 63.69 5.83 10.02 Traditional 28.23 73.28 4.24 -5.74
Basic 27.79 65.68 5.18 1.35 2000 Technology 22.96 63.29 6.87 6.89 Traditional 28.93 72.90 3.51 -5.34
Basic 28.83 66.85 4.62 -0.31 2001 Technology 22.78 69.55 8.18 -0.50 Traditional 28.49 71.46 3.36 -3.30
Basic 27.16 64.95 4.64 3.25 2002 Technology 21.65 66.42 8.55 3.38
24
Table 3 Descriptive Statistics (1991-2002)
Variable (unit) Total Traditional
Industry Basic
Industry
Technology-Intensive Industry
Growth rate of net sales (%) 11.24
(32.10)4.67
(30.98)6.78
(17.30) 18.23
(37.40)
Firm Age (years) 25.57
(10.47)29.18 (9.56)
29.32 (8.78)
20.99 (10.14)
Number of employees(logarithm)
6.54(1.00)
6.55 (0.98)
6.31 (1.03)
6.66 (0.98)
Capital Intensity (thousand NT / person)
3602.74(3834.36)
4124.52 (4295.29)
5002.03 (4605.81)
2463.40 (2438.60)
R&D ratio (%) 1.58
(2.47)0.41
(0.65)1.09
(1.76) 2.65
(3.08)
Exports ratio (%) 38.29
(32.48)24.74
(25.20)29.25
(24.07) 52.62
(35.12)
Invest ratio (%) 7.96
(31.56)5.87
(47.59)8.04
(20.32) 9.34
(22.20)
Debt to equity ratio (%) 45.08
(46.40)49.74
(54.26)49.85
(48.10) 39.23
(38.25)
Internal financing (%) 10.94
(24.97)6.39
(17.73)8.15
(16.65) 15.61
(31.45)
Return on total assets (%) 5.26
(7.48)3.31
(5.89)4.55
(5.40) 6.99
(8.93)Firm’s share of bankborrowing (%)
4.31(8.34)
5.60 (9.90)
5.69 (8.05)
2.65 (6.91)
Firm’s share of stock marketvalue (%)
5.01(9.18)
6.67 (11.52)
5.61 (7.41)
3.55 (7.96)
Bank-financing ratio (%) 22.55
(19.20)23.86
(17.11)23.60
(18.41) 21.06
(20.83)
Equity-financing ratio (%) 66.42
(25.37)70.45
(16.19)65.32
(18.33) 64.29
(32.58)
Bond-financing ratio (%) 4.86
(10.21)2.87
(5.94)4.59
(8.17) 6.37
(12.93)
Internal funds ratio (%) 6.17
(33.21)2.83
(14.86)6.49
(13.52) 8.28
(47.02)Note: Number in parentheses are standard error.
25
Table 4 Correlation Matrix Variables Y Age Size KL R&D Export InvestrGdpgr Profit Debt Sdebt Smar Bank Stock Bond InternalPrivateM2gdp Marcap Valtrade Structa Structs Y 1.00 Age -0.22 1.00 Size 0.06 0.19 1.00 KL -0.02 0.12 -0.12 1.00 R&D 0.05 -0.29 0.10 -0.09 1.00 Export 0.15 -0.41 0.11 -0.22 0.26 1.00 Investr 0.09 -0.07 0.11 0.16 0.13 0.02 1.00 Gdpgr 0.17 -0.07 0.09 -0.14 -0.10 -0.12 0.04 1.00 Profit 0.40 -0.22 0.17 -0.14 0.16 0.14 0.09 0.21 1.00 Debt -0.06 0.02 0.02 0.23 -0.12 -0.08 0.02 -0.11 -0.41 1.00Sdebt -0.06 0.38 0.37 0.19 -0.11 -0.25 0.04 0.06 -0.04 0.25 1.00Smar -0.04 0.37 0.47 0.05 -0.06 -0.21 0.03 0.11 0.15 -0.09 0.58 1.00Bank -0.02 0.02 0.01 0.18 -0.16 -0.08 0.05 -0.07 -0.39 0.76 0.26 -0.09 1.00Stock -0.17 0.12 -0.23 -0.08 -0.04 -0.06 -0.11 -0.05 -0.33 -0.20 -0.20 -0.10 -0.05 1.00 Bond 0.00 0.01 0.25 0.07 0.13 0.09 0.03 -0.09 -0.07 0.18 0.10 0.09 -0.05 0.10 1.00Internal 0.28 -0.25 0.04 -0.12 0.22 0.14 0.07 0.18 0.83 -0.39 -0.11 0.07 -0.39 -0.16 -0.11 1.00 Private 0.06 -0.03 -0.03 0.01 0.04 0.05 0.01 -0.02 0.06 -0.01 -0.05 -0.10 -0.05 0.00 0.05 0.08 1.00M2gdp -0.05 0.10 -0.11 0.18 0.12 0.16 -0.07 -0.75 -0.19 0.13 -0.09 -0.17 0.07 0.04 0.12 -0.17 0.25 1.00 Marcap -0.03 0.02 -0.07 0.08 0.10 0.11 -0.01 -0.39 -0.05 0.06 -0.08 -0.14 0.00 -0.02 0.09 -0.01 0.49 0.51 1.00 Valtrade 0.10 -0.01 -0.04 0.03 0.06 0.08 0.01 0.10 0.03 0.02 -0.06 -0.10 0.00 -0.06 0.07 0.06 0.39 0.18 0.59 1.00Structa 0.08 0.00 -0.05 0.05 0.07 0.09 0.00 0.01 0.00 0.04 -0.07 -0.11 0.02 -0.05 0.08 0.02 0.23 0.25 0.59 0.97 1.00Structs -0.04 0.03 -0.07 0.09 0.10 0.11 -0.02 -0.44 -0.07 0.07 -0.08 -0.13 0.01 -0.02 0.09 -0.03 0.33 0.55 0.97 0.57 0.63 1.00
26
Table 5 Regression Results (1) (2) (3) (4) (5) (6) (7) (8)
Constant -3.1386**
(0.0408) -1.1877**
(0.0325) -3.2059**
(0.0495)-2.7048**
(0.0408)-3.0631**
(0.0320)-3.1451**
(0.0198) -2.6948**
(0.0476) -2.4732**
(0.0517)Y[n-1] 0.0521**
(0.0010) -0.0033**
(0.0011) 0.0206**
(0.0013)-0.0386**
(0.0010)0.0526**
(0.0012)0.0518**
(0.0007) 0.0509**
(0.0008) 0.0457**
(0.0013)Age 0.1400**
(0.0017) 0.0912**
(0.0009) 0.1652**
(0.0016)0.1323**
(0.0011)0.1441**
(0.0015)0.1369**
(0.0008) 0.1370**
(0.0022) 0.1251**
(0.0018)Size 10.6886**
(0.1210) 10.9141**
(0.1223) 14.0160**
(0.2396)9.9297**
(0.1745)11.1278**
(0.1583)10.5252**
(0.1514) 11.2708**
(0.1265) 11.8546**
(0.1936)KL 0.0011**
(0.00002) 0.0013**
(0.00002) 0.0006**
(0.00001)0.0011**
(0.00002)0.0010**
(0.00001)0.0010**
(0.00001) 0.0009**
(0.00001)0.0009**
(0.00002)Lagged R&D
2.4670**
(0.0380) 3.4798**
(0.0635) 3.0979**
(0.0500)2.3925**
(0.0261)2.4198**
(0.0297)2.3986**
(0.0341) 2.3519**
(0.0297) 2.2505**
(0.0303)Export 0.3146**
(0.0029) 0.2522**
(0.0035) 0.0925**
(0.0040)0.3010**
(0.0038)0.2993**
(0.0032)0.3122**
(0.0021) 0.3215**
(0.0022) 0.2884**
(0.0035)Investr 0.0725**
(0.0011) 0.0544**
(0.0017) 0.0684**
(0.0009)0.0769**
(0.0007)0.0728**
(0.0012)0.0718**
(0.0010) 0.0683**
(0.0015) 0.0689**
(0.0017)Gdpgr 3.1614**
(0.0120) 2.2900**
(0.0086) 3.3152**
(0.0165)3.1292**
(0.0134)3.1658**
(0.0121) 3.1843**
(0.0084) 3.1397**
(0.0113)Debt -0.0353**
(0.0019)
Profit 2.1628**
(0.0092)
M2GDP -54.1170**
(0.5775)
Private 37.3801**
(0.3282)
Marcap -9.5524**
(0.1137)
Valtrade -1.0857**
(0.0124)
Structs -5.7821**
(0.0891)
Structa 0.3529**
(0.0411)
FDI -5.8749**
(0.1600) -9.2344**
(0.1542)FDI×IND1 11.5162**
(0.5307)FDI×IND2 5.1633**
(0.2569)Sargan test
p-values 0.86 0.91 0.70 0.80 0.82 0.80 0.83 0.84
Second-order serial correlation p-values
0.36 0.72 0.69 0.42 0.35 0.38 0.41 0.43
Notes: Numbers in parentheses are standard errors. **and *indicate significance levels of 5% and 10%, respectively .
27
Table 5 Regression Results (Continued) (9) (10) (11) (12) (13) (14) (15) (16)
Constant -3.1562**
(0.0298) -3.1432**
(0.0359) -3.1317**
(0.0472) -0.7142**
(0.0733) -0.4950**
(0.0840) -0.4286**
(0.0618) -0.6277**
(0.0841) -0.3734**
(0.0728) Y[n-1] 0.0496**
(0.0010) 0.0503**
(0.0010) 0.0492**
(0.0011) 0.0104**
(0.0011) 0.0105**
(0.0010) 0.0146**
(0.0008) 0.0126**
(0.0009) 0.0047**
(0.0009) Age 0.1433**
(0.0010) 0.1429**
(0.0012) 0.1459**
(0.0017) 0.0778**
(0.0020) 0.0721**
(0.0025) 0.0555**
(0.0023) 0.0788**
(0.0025) 0.0612**
(0.0022) Size 11.2846**
(0.1332) 11.2552**
(0.1412) 11.7711**
(0.1343) 11.3057**
(0.1965) 11.2667**
(0.2637) 11.3099**
(0.2647) 10.5938**
(0.1897) 11.2102**
(0.1897) KL 0.0011**
(0.00001) 0.0011**
(0.00001) 0.0011**
(0.00001)0.0009**
(0.00002)0.0009**
(0.00002)0.0010**
(0.00002) 0.0009**
(0.00002) 0.0009**
(0.00001)Lagged R&D
2.4845**
(0.0339) 2.5365**
(0.0454) 2.6481**
(0.0436) 2.7761**
(0.0656) 2.8318**
(0.0530) 3.1089**
(0.0783) 2.8834**
(0.0724) 2.9658**
(0.0828) Export 0.3069**
(0.0030) 0.3034**
(0.0035) 0.3035**
(0.0037) 0.2280**
(0.0048) 0.2354**
(0.0064) 0.2670**
(0.0077) 0.2281**
(0.0036) 0.2198**
(0.0040) Investr 0.0722**
(0.0018) 0.0713**
(0.0015) 0.0699**
(0.0013) 0.0519**
(0.0010) 0.0537**
(0.0015) 0.0669**
(0.0013) 0.0530**
(0.0013) 0.0553**
(0.0015) Gdpgr 3.1823**
(0.0073) 3.1922**
(0.0083) 3.1973**
(0.0107) 2.6329**
(0.0142) 2.6486**
(0.0112) 2.5822**
(0.0129) 2.6879**
(0.0129) 2.6227**
(0.0174) Sdebt 0.2485**
(0.0089) 0.2428**
(0.0194) 0.2560**
(0.0157)
Smar 0.9162**
(0.0150) 0.9041**
(0.0134) 1.8152**
(0.0507)
Bank 0.2055**
(0.0039) 0.2717**
(0.0063) 0.4764**
(0.0032) 0.1972**
(0.0034) 0.2036**
(0.0037) Stock -0.0018**
(0.0024) -0.0147**
(0.0026) -0.1608**
(0.0038) -0.0213**
(0.0029) 0.0053**
(0.0024) Bond -0.2095**
(0.0087) -0.2170**
(0.0108) 0.0141*
(0.0075) -0.1157**
(0.0085) -0.1937**
(0.0080) Internal 0.5117**
(0.0032) 0.5118**
(0.0036) 0.5864**
(0.0040) 0.5071**
(0.0027) 0.6311**
(0.0039) Sdebt×IND1 -0.0380
(0.0268)
Sdebt×IND2 0.0804**
(0.0279)
Smar×IND1 -1.5675**
(0.0535)
Smar×IND2 -1.2776**
(0.0470)
Bank×IND1 -0.1967**
(0.0146)
Bank×IND2 -0.1101**
(0.0106)
Stock×IND1 1.3739**
(0.0155)
Stock×IND2 0.3530**
(0.0118)
Bond×IND1 -0.8515**
(0.0258)
Bond×IND2 -0.2036**
(0.0230)
Internal× IND1
-0.3497**
(0.0099) Internal×
IND2 -0.2396**
(0.0064) Sargan test
p-values 0.85 0.84 0.84 0.91 0.93 0.87 0.88 0.86
Second-order serial
correlation p-values
0.40 0.39 0.39 0.52 0.53 0.69 0.51 0.64
Notes: Numbers in parentheses are standard errors. **and *indicate significance levels of 5% and 10%, respectively .
28